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Abstract The solar corona is extremely dynamic. Every leap in observational capabilities has been accompanied by unexpected revelations of complex dynamic processes. The ever more sensitive instruments now allow us to probe events with increasingly weaker energetics. A recent leap in the low-frequency radio solar imaging ability has led to the discovery of a new class of emissions, namely weak impulsive narrowband quiet Sun emissions (WINQSEs). They are hypothesized to be the radio signatures of coronal nanoflares and could potentially have a bearing on the long standing coronal heating problem. In view of the significance of this discovery, this work has been followed up by multiple independent studies. These include detecting WINQSEs in multiple data sets, using independent detection techniques and software pipelines, and looking for their counterparts at other wavelengths. This work focuses on investigating morphological properties of WINQSEs and also improves upon the methodology used for detecting WINQSEs in earlier works. We present a machine learning-based algorithm to detect WINQSEs, classify them based on their morphology, and model the isolated ones using 2D Gaussians. We subject multiple data sets to this algorithm to test its veracity. Interestingly, despite the expectations of their arising from intrinsically compact sources, WINQSEs tend to be resolved in our observations. We propose that this angular broadening arises due to coronal scattering. Hence, WINQSEs can provide ubiquitous and ever-present diagnostic of coronal scattering (and, in turn, coronal turbulence) in the quiet Sun regions, which has not been possible until date. 

Due to improvements in high-performance computing (HPC) capabilities, many of today’s applications produce petabytes worth of data, causing bottlenecks within the system. Importance-based sampling methods, including our spatio-temporal hybrid data sampling method, are capable of resolving these bottlenecks. While our hybrid method has been shown to outperform existing methods, its effectiveness relies heavily on user parameters, such as histogram bins, error threshold, or number of regions. Moreover, the throughput it demonstrates must be higher to avoid becoming a bottleneck itself. In this article, we resolve both of these issues. First, we assess the effects of several user input parameters and detail techniques to help determine optimal parameters. Next, we detail and implement accelerated versions of our method using OpenMP and CUDA. Upon analyzing our implementations, we find 9.8× to 31.5× throughput improvements. Next, we demonstrate how our method can accept different base sampling algorithms and the effects these different algorithms have. Finally, we compare our sampling methods to the lossy compressor cuSZ in terms of data preservation and data movement. 

Abstract Weak Impulsive Narrowband Quiet Sun Emissions (WINQSEs) are a newly discovered class of radio emission from the solar corona. These emissions are characterized by their extremely impulsive, narrowband, and ubiquitous nature. We have systematically been working on their detailed characterization, including their strengths, morphologies, temporal characteristics, energies, etc. This work is the next step in this series and focuses on the spectral nature of WINQSEs. Given that their strength is only a few percent of the background solar emission, we have adopted an extremely conservative approach to reliably identify WINQSES. Only a handful of WINQSEs meet all of our stringent criteria. Their flux densities lie in the 20–50 Jy range and they have compact morphologies. For the first time, we estimate their bandwidths and find them to be less than 700 kHz, consistent with expectations based on earlier observations. Interestingly, we also find similarities between the spectral nature of WINQSEs and the solar radio spikes. This is consistent with our hypothesis that the WINQSEs are the weaker cousins of the type III radio bursts and are likely to be the low-frequency radio counterparts of the nanoflares, originally hypothesized as a possible explanation for coronal heating. 

Abstract In this work, we study a class of recently discovered meter-wave solar transients referred to as Weak Impulsive Narrowband Quiet Sun Emission (WINQSEs). Their strength is a few percent of the quiet Sun background and is characterized by their very impulsive, narrowband, and ubiquitous presence in quiet Sun regions. Mondal et al. (2020) hypothesized that these emissions might be the radio counterparts of nanoflares, and their potential significance warrants detailed studies. Here we present an analysis of data from an extremely quiet time and with improved methodology over the previous work. As before, we detect numerous WINQSEs, which we have used for their further characterization. Their key properties, namely, their impulsive nature and ubiquitous presence in the quiet Sun, are observed in these data as well. Interestingly, we also find some of the observed properties to differ significantly from the earlier work. With this demonstration of routine detection of WINQSEs, we hope to engender interest in the larger community to build a deeper understanding of WINQSEs. 

A significant challenge on an exascale computer is the speed at which we compute results exceeds by many orders of magnitude the speed at which we save these results. Therefore the Exascale Computing Project (ECP) ALPINE project focuses on providing exascale-ready visualization solutions including in situ processing. In situ visualization and analysis runs as the simulation is run, on simulations results are they are generated avoiding the need to save entire simulations to storage for later analysis. The ALPINE project made post hoc visualization tools, ParaView and VisIt, exascale ready and developed in situ algorithms and infrastructures. The suite of ALPINE algorithms developed under ECP includes novel approaches to enable automated data analysis and visualization to focus on the most important aspects of the simulation. Many of the algorithms also provide data reduction benefits to meet the I/O challenges at exascale. ALPINE developed a new lightweight in situ infrastructure, Ascent. 

Due to I/O bandwidth limitations, intelligent in situ data reduction methods are needed to enable post-hoc workflows. Current state-of-the-art sampling methods save data points if they deem them spatially or temporally important. By analyzing the properties of the data values at each time-step, two consecutive steps may be very similar. This research follows the notion that if neighboring time-steps are very similar, samples from both are unnecessary, which leaves storage for adding more useful samples. Here, we present an investigation of the combination of spatial and temporal sampling to drastically reduce data size without the loss of valuable information. We demonstrate that, by reusing samples, our reconstructed data set reduces the overall data size while achieving a higher post-reconstruction quality over other reduction methods.